Chapman & Hall/Distributed Computing and Intelligent Data Analytics Series – serie

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features· Comprehensive introduction to cloud architecture and its service models.· Vulnerability and issues in cloud SAAS, PAAS and IAAS· Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models· Detailed study of optimization techniques, and fault management techniques in multi layered cloud.· Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network.· Advanced study of algorithms using artificial intelligence for optimization in cloud· Method for power efficient virtual machine placement using neural network in cloud· Method for task scheduling using metaheuristic algorithms.· A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud.Key Features· Comprehensive introduction to cloud architecture and its service models.· Vulnerability and issues in cloud SAAS, PAAS and IAAS· Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models· Detailed study of optimization techniques, and fault management techniques in multi layered cloud.· Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network.· Advanced study of algorithms using artificial intelligence for optimization in cloud· Method for power efficient virtual machine placement using neural network in cloud· Method for task scheduling using metaheuristic algorithms.· A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment.This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

The book Computer Applications in Engineering and Management is about computer applications in management, electrical engineering, electronics engineering, and civil engineering. It covers the software tools for office automation, introduces the basic concepts of database management, and provides an overview about the concepts of data communication, internet, and e-commerce. Additionally, the book explains the principles of computing management used in construction of buildings in civil engineering and the role of computers in power grid automation in electronics engineering.Features Provides an insight to prospective research and application areas related to industry and technology Includes industry-based inputs Provides a hands-on approach for readers of the book to practice and assimilate learningThis book is primarily aimed at undergraduates and graduates in computer science, information technology, civil engineering, electronics and electrical engineering, management, academicians, and research scholars.

Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.Features:A diverse collection of important and cutting-edge topics covered in a single volumeSeveral chapters on cybersecurity, an extremely active research areaRecent research results from leading researchers and some pointers to future advancements in methodologyDetailed experimental results obtained from standard data setsThis book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.

Artificial Intelligence: Applications and Innovations is a book about the science of artificial intelligence (AI). AI is the study of the design of intelligent computational agents. This book provides a valuable resource for researchers, scientists, professionals, academicians and students dealing with the new challenges and advances in the areas of AI and innovations. This book also covers a wide range of applications of machine learning such as fire detection, structural health and pollution monitoring and control. Key FeaturesProvides insight into prospective research and application areas related to industry and technologyDiscusses industry- based inputs on success stories of technology adoptionDiscusses technology applications from a research perspective in the field of AIProvides a hands- on approach and case studies for readers of the book to practice and assimilate learningThis book is primarily aimed at graduates and post- graduates in computer science, information technology, civil engineering, electronics and electrical engineering and management.

The book Computer Applications in Engineering and Management is about computer applications in management, electrical engineering, electronics engineering, and civil engineering. It covers the software tools for office automation, introduces the basic concepts of database management, and provides an overview about the concepts of data communication, internet, and e-commerce. Additionally, the book explains the principles of computing management used in construction of buildings in civil engineering and the role of computers in power grid automation in electronics engineering.Features Provides an insight to prospective research and application areas related to industry and technology Includes industry-based inputs Provides a hands-on approach for readers of the book to practice and assimilate learningThis book is primarily aimed at undergraduates and graduates in computer science, information technology, civil engineering, electronics and electrical engineering, management, academicians, and research scholars.

Artificial Intelligence: Applications and Innovations is a book about the science of artificial intelligence (AI). AI is the study of the design of intelligent computational agents. This book provides a valuable resource for researchers, scientists, professionals, academicians and students dealing with the new challenges and advances in the areas of AI and innovations. This book also covers a wide range of applications of machine learning such as fire detection, structural health and pollution monitoring and control. Key FeaturesProvides insight into prospective research and application areas related to industry and technologyDiscusses industry- based inputs on success stories of technology adoptionDiscusses technology applications from a research perspective in the field of AIProvides a hands- on approach and case studies for readers of the book to practice and assimilate learningThis book is primarily aimed at graduates and post- graduates in computer science, information technology, civil engineering, electronics and electrical engineering and management.

Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.Features:A diverse collection of important and cutting-edge topics covered in a single volumeSeveral chapters on cybersecurity, an extremely active research areaRecent research results from leading researchers and some pointers to future advancements in methodologyDetailed experimental results obtained from standard data setsThis book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.

This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques.Focuses on virtual machine placement and migration techniques for cloud data centersPresents the role of machine learning and metaheuristic approaches for optimisation in cloud computing servicesIncludes application of placement techniques for quality of service, performance, and reliability improvementExplores data center resource management, load balancing and orchestration using machine learning techniquesAnalyses dynamic and scalable resource scheduling with a focus on resource managementThe text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.

This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques.Focuses on virtual machine placement and migration techniques for cloud data centersPresents the role of machine learning and metaheuristic approaches for optimisation in cloud computing servicesIncludes application of placement techniques for quality of service, performance, and reliability improvementExplores data center resource management, load balancing and orchestration using machine learning techniquesAnalyses dynamic and scalable resource scheduling with a focus on resource managementThe text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.

Computer vision is an effective solution in a diverse range of real-life applications. With the advent of the machine and deep learning paradigms, this book adopts machine and deep learning algorithms to leverage digital image processing for designing accurate biometrical applications. In this aspect, it presents the advancements made in computer vision to biometric applications design approach using emerging technologies. It discusses the challenges of designing efficient and accurate biometric-based systems, which is a key issue that can be tackled via computer vision-based techniques.Key FeaturesDiscusses real-life applications of emerging techniques in computer vision systemsOffers solutions on real-time computer vision and biometrics applications to cater to the needs of current industryPresents case studies to offer ideas for developing new biometrics-based productsOffers problem-based solutions in the field computer vision and real-time biometric applications for secured human authenticationWorks as a ready resource for professionals and scholars working on emerging topics of computer vision for biometricsThe book is for academic researchers, scholars and students in Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, management, academicians, researchers, scientists and industry people working on computer vision and biometrics applications.

Computer vision is an effective solution in a diverse range of real-life applications. With the advent of the machine and deep learning paradigms, this book adopts machine and deep learning algorithms to leverage digital image processing for designing accurate biometrical applications. In this aspect, it presents the advancements made in computer vision to biometric applications design approach using emerging technologies. It discusses the challenges of designing efficient and accurate biometric-based systems, which is a key issue that can be tackled via computer vision-based techniques.Key FeaturesDiscusses real-life applications of emerging techniques in computer vision systemsOffers solutions on real-time computer vision and biometrics applications to cater to the needs of current industryPresents case studies to offer ideas for developing new biometrics-based productsOffers problem-based solutions in the field computer vision and real-time biometric applications for secured human authenticationWorks as a ready resource for professionals and scholars working on emerging topics of computer vision for biometricsThe book is for academic researchers, scholars and students in Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, management, academicians, researchers, scientists and industry people working on computer vision and biometrics applications.

This book explores the transformative potential of artificial intelligence (AI) and data engineering in shaping the future of smart cities. With the rapid growth of urbanisation, cities are facing complex challenges such as efficient resource management, urban mobility, environmental sustainability, and citizen well-being. It offers authoritative insights and perspectives brought together by leading experts to delve into the latest advancements and applications of AI and data engineering in addressing urban challenges.• Includes practical insights, case studies and real-world examples of how AI and data engineering are being applied in smart city initiatives• Presents the role of AI in enhancing urban infrastructure, optimizing energy consumption, enabling intelligent transportation systems, and empowering citizen participation.• Showcases how AI and data engineering can revolutionise urban landscapes and pave the way for sustainable and intelligent cities• Discusses the ethical and privacy concerns related to the use of these technologies in cities, highlighting the importance of responsible and sustainable development.• Presents an interdisciplinary approach, exploring the intersection of AI and data engineering with fields such as urban planning, transportation, energy and health.The book is primarily aimed at researchers, practitioners, and students in the fields of computer science, engineering and urban planning. It is also relevant to policymakers, government officials, and other professionals involved in the development and implementation of smart city solutions.

Computer vision is an effective solution in a diverse range of real-life applications. With the advent of the machine and deep learning paradigms, this book adopts machine and deep learning algorithms to leverage digital image processing for designing accurate biometrical applications. In this aspect, it presents the advancements made in computer vision to biometric applications design approach using emerging technologies. It discusses the challenges of designing efficient and accurate biometric-based systems, which is a key issue that can be tackled via computer vision-based techniques.Key FeaturesDiscusses real-life applications of emerging techniques in computer vision systemsOffers solutions on real-time computer vision and biometrics applications to cater to the needs of current industryPresents case studies to offer ideas for developing new biometrics-based productsOffers problem-based solutions in the field computer vision and real-time biometric applications for secured human authenticationWorks as a ready resource for professionals and scholars working on emerging topics of computer vision for biometricsThe book is for academic researchers, scholars and students in Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, management, academicians, researchers, scientists and industry people working on computer vision and biometrics applications.